A super resolution technology is known as an example of a technology to generate the restored image from the blurred image. A method using a dictionary which learns cases in which a low resolution image is associated with a high resolution image is especially called a learning based super resolution technology in the super resolution technology. One example of the learning based super resolution technology is described in non-patent document 1.
The learning based super resolution technology described in non-patent document 1 performs the following method (hereinafter, referred to as “super-resolution process”).
Namely, the super-resolution process receives an input image which is a low resolution image.
Next, the super-resolution process increases the number of pixels of the input image by an interpolation method and generates a temporary high resolution image.
Further, the super-resolution process generates a low frequency component from one increasing the number of pixels of the input image.
The super-resolution process cuts out a low frequency patch from the generated low frequency component and calculates a low frequency feature quantity from the low frequency patch.
The super-resolution process searches a predetermined number of low frequency feature quantities from a dictionary in order of increasing distance from the calculated low frequency feature quantity, and reads the high frequency feature quantity paired with the searched low frequency feature quantity.
Then, the super-resolution process selects one high frequency feature quantity on the basis of the distance at the time of the search, a consistency with an adjacent high frequency block, a co-occurrence probability of the low frequency feature quantity and the high frequency feature quantity separately learned at a learning stage, or the like.
The technology described in non-patent document 1 reduces a memory amount and suppresses a calculation cost by using a dictionary structure with one-to-many relation in which the low frequency feature quantities that are mutually similar to each other are aggregated to one representative.
[Non-patent document 1] Yasunori Taguchi, Toshiyuki Ono, Takeshi Mita, Takashi Ida, “A Learning Method of Representative Examples for Image Super-Resolution by Closed-Loop Training”, The journal of the Institute of Electronics, Information and Communication Engineer D, Information System, Vol. J92-D No. 6, pp. 831-842, Jun. 1, 2009.